面向畜禽加工的智能化装备与技术研究现状和发展趋势
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摘要
现阶段,智能化加工装备与技术代表着生产力,是提高生产效率、转变发展方式的物质基础,智能化装备技术在畜禽加工过程中的应用在保证稳定、可靠的生产过程的同时,有着显著的经济效益。总结了智能化装备在畜禽屠宰、分割和分级等加工过程中的应用,归纳了国内外学者在机器视觉、光谱检测、多种技术融合、X射线CT成像和超声波成像等智能化技术在畜禽加工领域的诸多研究成果,分析了当前畜禽加工中存在的智能装备不系统、智能化技术不成熟的问题现状,展望了未来智能装备技术在畜禽加工中设备类型标准、多样化,提高设备集成化水平,促进技术融合等发展趋势,为畜禽加工智能化装备技术研究与行业智能化发展提供相关信息和参考。
At present,intelligent processing equipment and technology represent productivity,which is the material basis for improving production efficiency and transforming development mode. The intelligent equipment technology in the processing of livestock and poultry could ensure stable and reliable production,and have significant economic benefits. Therefore,it has become the focus of the international equipment technology industry. The advantage of smart equipment is that it improves the manual work environment,replaces the heavy,boring and manual labor of the workers,improves product quality and the production efficiency,and reduces contact cross-contamination. Application of intelligent equipment in the processing of slaughtering,segmentation and grading of livestock and poultry was summarized,intelligent techniques including machine vision,spectrum detection,multi-technology fusion,X-ray CT imaging and ultrasonic imaging were concluded,current problems that intelligent equipment was not systematic and intelligent technology was not immature on the processing of livestock and poultry were aralyzed,and the future of intelligent equipment technology in livestock and poultry processing was prospected: standardizing and diversifying equipment type; improving level of equipment integration; and promoting development trend of technology convergence. By comparing research progress and development bottlenecks of different technologies,the next research direction was proposed. It can provide relevant information and reference for research and development of intelligent equipment technology on livestock and poultry processing.
At present,intelligent processing equipment and technology represent productivity,which is the material basis for improving production efficiency and transforming development mode. The intelligent equipment technology in the processing of livestock and poultry could ensure stable and reliable production,and have significant economic benefits. Therefore,it has become the focus of the international equipment technology industry. The advantage of smart equipment is that it improves the manual work environment,replaces the heavy,boring and manual labor of the workers,improves product quality and the production efficiency,and reduces contact cross-contamination. Application of intelligent equipment in the processing of slaughtering,segmentation and grading of livestock and poultry was summarized,intelligent techniques including machine vision,spectrum detection,multi-technology fusion,X-ray CT imaging and ultrasonic imaging were concluded,current problems that intelligent equipment was not systematic and intelligent technology was not immature on the processing of livestock and poultry were aralyzed,and the future of intelligent equipment technology in livestock and poultry processing was prospected: standardizing and diversifying equipment type; improving level of equipment integration; and promoting development trend of technology convergence. By comparing research progress and development bottlenecks of different technologies,the next research direction was proposed. It can provide relevant information and reference for research and development of intelligent equipment technology on livestock and poultry processing.
引文
[1]王素梅,程文新,王继鹏,等.现代生猪屠宰窒晕技术介绍[J].肉类工业,2013(3):30-31.WANG Sumei,CHENG Wenxin,WANG Jipeng,et al. Introduction of modern stuning method in pig shaughterung[J]. Meat Industry,2013(3):30-31.
[2]马丁·冯·文斯兰沃维斯,阿奇姆·许,凯琳·冯·霍莱本,等. Inarco系统米达斯生猪电击晕设备福利性及肉质现场研究第一部分:电流特点和击晕有效性[J].肉类工业,2009(6):2-6.
[3] BANSS. BANSS BRT High frequency constant current stunning technology[EB/OL].[2019-02-09]. https://banss. de/en/#slaughtering-technology-pig-stunning/.
[4]丛明,王冠雄,Peter Xu.屠宰机器人的研究现状与发展[J].机器人技术与应用,2013(1):18-23.CONG Ming,WANG Guanxiong,Peter Xu. Research and development of the slaughter robot[J]. Robot Technique and Application,2013(1):18-23.
[5] BANSS. BANSS DDM[EB/OL].[2019-02-09]. https://banss.de/en/#slaughtering-technology-pig-dehairing/.
[6] BANSS. BANSS BE[EB/OL].[2019-02-09]. https://banss. de/en/#slaughtering-technology-cattle-dehiding/.
[7] TREVELYAN J P. Robots for shearing sheep. Shear magic.[J].Oxford University Press,1992.
[8] PURNELL G. Robotic equipment in the meat industry[J]. Meat Science,1998,49(S1):S297.
[9] FRONTMATEC. Automatic Primal Cutting[EB/OL].[2019-02-09].https://www. frontmatec. com/en/pork-solutions/primal-cutting.
[10]任涛,李伟,徐开春,等.猪体自动劈半机的研发[J].肉类工业,2017(9):49-56.REN Tao, LI Wei, XU Kaichun, et al. Research and development of pig automatic half splitting machine[J]. Meat Industry,2017(9):49-56.
[11] MEYN. Vent cutter M3. 0[EB/OL].[2019-02-09]. https://www. meyn. com/products/evisceration/vent-cutter-m3-0.
[12]王丽红.基于数字化设计的家禽取内脏机关键技术研究[D].北京:中国农业机械化科学研究院,2011.WANG Lihong. Study on key technology of poultry eviscerator based on digital design[D]. Beijing:Chinese Academy of Agricultural Mechanization Science,2011.
[13] MACHINEFABRIEK MEYN B V. Method and apparatus for eviscerating poultry:EP0574617A1[P]. 1992-06-17.
[14] STORK PMT B V. Method and devide for processing a cluster of organs from a slaughtered animal:EP0587253B1[P]. 1997-04-16.
[15] MEYN. Flex Plus cut up line M3. 0[EB/OL].[2019-02-09].https://www. meyn. com/products/cut-up/flex-plus-cut-up-linem3-0.
[16]王猛,李阳阳,叶金鹏.家禽自动掏膛机械手的发展和应用现状[J].农产品加工(学刊),2014(5):62-64.WANG Meng,LI Yangyang,YE Jinpeng. Development and application of the poultry eviscerating manipulator[J]. Academic Periodical of Farm Products Processing,2014(5):62-64.
[17]邢东杰,张奎彪,张文辉.一种家禽自动掏膛机:CN202999192U[P]. 2013-06-19.
[18]王斌. L-10000型家禽自动掏膛生产线被评为全国重大装备首台套示范项目[N].中国工业报,2015-07-23.
[19]王丽红,阎楚良,叶金鹏,等. QNZ15型家禽自动取内脏机设计与试验[J].农业机械学报,2010,41(S1):220-224.WANG Lihong,YAN Chuliang,YE Jinpeng,et al. Design and experiment of QNZ15 automatic poultry eviserator[J]. Transactions of the Chinese Society for Agricultural Machinery,2010,41(S1):220-224.
[20]鲍秀兰,张磊,王树才.家禽净膛机械手末端执行器的设计及运动学分析[J].华中农业大学学报,2017,36(4):117-124.BAO Xiulan,ZHANG Lei,WANG Shucai. Design and kinematics analyses of manipulator end effector for eviscerated poultry[J].Journal of Huazhong Agricultural University,2017,36(4):117-124.
[21]熊利荣,于阳,王树才.带有触觉系统的家禽屠宰净膛机械手的设计[J].华中农业大学学报,2016,35(6):142-146.XIONG Lirong, YU Yang, WANG Shucai. Designing intelligent manipulator with haptic system for poultry slaughtering and evisceration[J]. Journal of Huazhong Agricultural University,2016,35(6):142-146.
[22] FOSS. FoodScan[EB/OL].[2019-02-09]https://www. fossanalytics. com/zh-cn/products/foodscan-meat-analyser.
[23] FRONTMATEC. Carcass Grading[EB/OL].[2019-02-09]. https://www. frontmatec. com/en/pork-solutions/clean-line-chillroom/carcass-grading.
[24] Uni Green Scheme. Destron PG-100 Electronic Grading Probe Kit For Pork Meat Testing Lab Equipment[EB/OL].[2019-02-09].https://shop. unigreenscheme. co. uk/other-lab-equipment/destronpg-100-electronic-grading-probe-kit-for-pork-meat-testing-lab-equipment-fyyn8.
[25] SATHER-AP, JONES-SDM, ROBERTSON-WM. The effect of genotype on pre-dicted lean yield in heavy pig carcasses using the Hennessy grading probe,the DESTRON PG-100 and Fat-O-Meat'er electronic grading probes[J]. Canadian Journal of Animal Science,1995,69:93-101.
[26] HENNESSY. HGS world's leading meat grading system[EB/OL].[2019-02-09]. http://www. hennessy-technology. com/.
[27] KEMPSTER A J. An evaluation of the hennessy grading probe and the SFK Fat-O-Meater for use in pig carcass classification and grading[J]. Animal Science,1985,40(2):323-329.
[28] GISPERT M,GOU P,DIESTER A,et al. Bias and future trends of pig carcass classification methods[J]. Food Chemistry,2000,69(4):457-460.
[29]丁有河,王华,王道路,等.畜胴体自动分级系统开发[J].肉类工业,2017(6):46-48.DING Youhe,WANG Hua,WANG Daolu,et al. Development of automatic grading system of livestock carcass[J]. Meat Industry,2017(6):46-48.
[30] SATHER A P,DRC B,SDM J. Real-time ultrasound image analysis for the estimation of carcass yield and pork quality[J]. Canadian Veterinary Journal La Revue Veterinaire Canadienne,1996,76(1):55-62.
[31] FRONTMATEC. AutoFom[EB/OL].[2019-02-09]. https://www. frontmatec. com/en/other/instruments/carcass-grading-traceability/autofom-iii.
[32] GOLDENBERG A A,ANANTHANARAYANAN S P. An approach to automation of pork grading[J]. Food Research International,1994,27(2):191-193.
[33] UNIPACK.Техническийвзгляднапродукты[EB/OL].(2013-10-11)[2019-02-09]. https://article. unipack. ru/46991/.
[34] TAKATANI S,GRAHAM M D. Theoretical analysis of diffuse reflectance from a two-layer tissue model[J]. IEEE Transactions on Biomedical Engineering,1979,BME-26(12):656-664.
[35] KAY S,HEINAR S,HEINZ D,et al. A portable 671 nm Raman sensor system for rapid meat spoilage identification[J]. Vibrational Spectroscopy,2012,62:70-76.
[36]李翠玲,彭彦昆,汤修映.基于多光谱成像技术的猪肉新鲜度无损快速检测装置[J].农业机械学报,2012,43(S1):202-206.LI Cuiling,PENG Yankun,TANG Xiuying. Device for rapid nondestructive detection of pork freshness based on multispectral imaging technology[J]. Transactions of the Chinese Society for Agricultural Machinery,2012,43(S1):202-206.
[37]海铮,基于电子鼻的牛肉新鲜度检测[D].杭州:浙江大学,2006.HAI Zheng. Detection of beef freshness by an electronic nose[D].Hangzhou:Zhejiang University,2006.
[38]林琬,彭彦昆,王彩萍.便携式生鲜肉品质无损快速检测装置的设计[J].农业工程学报,2014,30(7):243-249.LIN Wan,PENG Yankun,WANG Caiping. Design of portable device for rapid nondestructive detection of fresh meat quality[J].Transactions of the Chinese Society of Agricultural Engineering,2014,30(7):243-249.
[39]林琬.轻简便携式生鲜肉品质无损检测装置的研发[D].北京:中国农业大学,2014.LIN Wan. Development of lightweight portable device for rapid nondestructive detection of fresh meat quality[D]. Beijing:China Agricultural University,2014.
[40]魏文松,彭彦昆.手持式生鲜肉品质参数无损检测装置研究[J].农业机械学报,2016,47(S1):324-331.WEI Wensong,PENG Yankun. Research on hand-held device for nondestructive detection of meat quality parameters[J]. Transactions of the Chinese Society for Agricultural Machinery,2016,47(S1):324-331.
[41] GERRARD D E,GAO X,TAN J. Beef marbling and color score determination by image processing[J]. Journal of Food Science,1996,61(1):145-148.
[42] CANNELL R C,TATUM J D,BELK K E,et al. Dual-component video image analysis system(VIASCAN)as a predictor of beef carcass red meat yield percentage and for augmenting application of USDA yield grades[J]. Journal of Animal Science,1999,77(11):2 942-2 950.
[43] SHIRANITA K,HAYASHI K,OTSUBO A,et al. Grading meat quality by image processing[J]. Pattern Recognition,2000,33(1):97-104.
[44] FRONTMATEC. Carcass Grading[EB/OL].[2019-02-09]. https://www. frontmatec. com/en/beef-solutions/clean-line-chill-room/carcass-grading.
[45]陈丽,张德权.羊胴体分级技术研究现状及趋势[J].食品科技,2010(9):146-150.CHEN Li,ZHANG Dequan. The present situation and trends of the mutton classification technology[J]. Food Science and Technology,2010(9):146-150.
[46]郭楠,王丽红,丁有河,等.气动式羊胴体自动分级系统开发[J].肉类工业,2017(11):49-51.GUO Nan,WANG Lihong,DING Youhe,et al. Development of pneumatic type automatic classification system of sheep carcass[J].Meat Industry,2017(11):49-51.
[47] MEYN. Bird counter vision M1. 0[EB/OL].[2019-02-09]. https://www. meyn. com/products/slaughtering/bird-counter-visionm1-0.
[48] LINCO. Classif EYE[EB/OL].[2019-02-09]. https://www. baader.com/en/products/poultry_processing/distribution/classifeye/index. html.
[49]王丽红,叶金鹏,王子戡,等.畜禽胴体分级技术[J].肉类工业,2014(10):37-41.WANG Lihong,YE Jinpeng,WANG Zikan,et al. Grading technology of livestock and poultry carcass[J] Meat Industry,2014(10):37-41.
[50]张奎彪.中国家禽加工设备“十三五”期间发展趋势[J].肉类工业,2016(11):35-41.ZHANG Kuibiao. The development trend of Chinese poultry processing equipment during the 13th Five-Year Plan period[J]. Meat Industry,2016(11):35-41.
[51]毕然,王家敏,张建喜,等.生猪屠宰监管技术系统设计与实现研究[J].中国农学通报,2012,28(17):57-62.BI Ran,WANG Jiamin,ZHANG Jianxi,et al. Design and application of safe quality supervision system for live-pig slaughtering[J]. Chinese Agricultural Science Bulletin,2012,28(17):57-62.
[52]姬五胜,郭宏,张丰臣.基于Zig Bee和RFID技术的生猪屠宰可溯源系统的设计与实现[J].天津职业技术师范大学学报,2014,24(2):1-6.JI Wusheng,GUO Hong, ZHANG Fengchen. Design and implement of pig slaughtering traceability system based on RFID and ZigBee technology[J]. Journal of Tianjin University of Technology and Education,2014,24(2):1-6.
[53]陈长喜,张宏福,飞颉经纬.肉鸡产业技术体系生产监测与产品质量可追溯平台设计[J].农业机械学报,2010,41(8):100-106.CHEN Changxi, ZHANG Hongfu, FEI Xiejingwei. Traceability platform design of production monitoring and products quality for broilers industry technology system[J]. Transactions of the Chinese Society for Agricultural Machinery,2010,41(8):100-106.
[54]王树才,陶凯,李航.基于机器视觉定位的家禽屠宰净膛系统设计与试验[J].农业机械学报,2018,49(1):335-343.WANG Shucai, TAO Kai, LI Hang. Design and experiment of poultry eviscerator system based on machine vision positioning[J].Transactions of the Chinese Society for Agricultural Machinery,2018,49(1):335-343.
[55] MCDONALD T P,CHEN Y R. Visual characterization of marbling in beef ribeyes and its relationship to taste parameters[J]. Transactions of the Asae,1990,34(6):2 499-2 504.
[56] LI J,TAN J,MARTZ F A,et al. Image texture features as indicators of beef tenderness[J]. Meat Science,1999,53(1):17.
[57] LI J,TAN J,SHATADAL P. Classification of tough and tender beef by image texture analysis[J]. Meat Science,2001,57(4):341-346.
[58] GERRARD D E,GAO X,TAN J. Beef marbling and color score determination by image processing[J]. Journal of Food Science,1996,61(1):145-148.
[59]罗明,朱砺.屠宰加工线上瘦肉型猪胴体瘦肉率估测方法的研究[J].四川农业大学学报,2002,20(2):153-155.LUO Ming,ZHU Li. Study on estimating lean percent of carcass in slaughterhouse[J]. Journal of Sichuan Agricultural University,2002,20(2):153-155.
[60] AMWYLE,CANNELL R C,BELK K E,et al. An evaluation of the pro-totype portable hunterLab video imaging system as a tool to predict tenderness of beef carcasses using objective measure of lean and fatcolor[R]. BeefProgram Report,1999.
[61]于铂,郑丽敏,任发政,等.利用图像处理技术估算猪肉等级评价指标的应用研究[J].肉类研究,2004(3):41-44.
[62] GWARTNEY B L,GAO X,TAN J,et al. Determining fat content in ground beef via color image processing[J]. Journal of Muscle Foods,1996,7(4):11.
[63]郑丽敏,于铂,唐毅,等.利用图像处理技术自动估算猪胴体参数[J].计算机应用研究,2007,24(1):203-206.ZHENG Limin,YU Bo,TANG Yi,et al. Evaluating parameters of hogs's carcass using image processing technology[J]. Application Research of Computers,2007,24(1):203-206.
[64]唐毅.基于机器视觉技术的猪胴体等级在线评定系统[D].北京:中国农业大学,2006.TANG Yi. Online system evaluating pork carcass grade based machine vision[D]. Beijing:China Agricultural University,2006.
[65]丁筱玲,吴玉红,周田田,等.基于机器视觉技术的鸡翅质量预测[J].江苏农业科学,2017(9):216-220.
[66]陈坤杰,李航,于镇伟,等.基于机器视觉的鸡胴体质量分级方法[J].农业机械学报,2017,48(6):290-295,372.CHEN Kunjie,LI Hang,YU Zhenwei,et al. Grading of chicken carcass weight based on machine vision[J]. Transactions of the Chinese Society for Agricultural Machinery,2017,48(6):290-295,372.
[67] BERRANG M E,Smith D P,Windham W R,et al. Effect of intestinal content contamination on broiler carcass Campylobacter counts[J]. Journal of Food Protection,2004,67(2):235.
[68] WEIL S E,PARK B,Y. R C,et al. Integration of Visible/NIR spectroscopy and multispectral imaging for poultry carcass inspection[C]//Photonics for Industrial Applications. International Society for Optics and Photonics,1995.
[69] PARK B,YOON S C,WINDHAM W R,et al. Line-scan hyperspectral imaging for real-time in-line poultry fecal detection[J].Sensing&Instrumentation for Food Quality&Safety,2011,5(1):25-32.
[70]孙啸,逄滨,刘德营,等.基于高光谱图像光谱信息的牛肉大理石花纹分割[J].农业机械学报,2013,44(S1):177-181.SUN Xiao,PANG Bin,LIU Deying,et al. Beef marbling segmentation based on hyperspectral imaging[J]. Transactions of the Chinese Society for Agricultural Machinery, 2013, 44(S1):177-181.
[71]廖宜涛,樊玉霞,伍学千,等.猪肉肌内脂肪含量的可见/近红外光谱在线检测[J].农业机械学报,2010,41(9):104-107,137.LIAO Yitao,FAN Yuxia,WU Xueqian,et al. On-line prediction of intramuscular fat content in pork muscle with visible/near-infrared spectroscopy[J]. Transactions of the Chinese Society for Agricultural Machinery,2010,41(9):104-107,137.
[72]张嫱,郑明媛,张伟,等.高光谱成像技术在禽类产品品质无损检测中的研究进展[J].食品工业科技,2013,34(14):358-362.ZHANG Qiang,ZHENG Mingmin,ZHANG Wei,et al. Research progress in nondestructive detection of poultry products quality based on hyperspectral imaging[J]. Science and Technology of Food Industry,2013,34(14):358-362.
[73] NAKARIYAKUL S,CASASENT D P. Improved forward floating selection algorithm for chicken contaminant detection in hyperspectral imagery[C]//Algorithms and Technologies for Multispectral,Hyperspectral,and Ultraspectral Imagery XIII. International Society for Optics and Photonics,2007.
[74]赵进辉,涂冬成,欧阳静怡,等.利用高光谱图像技术检测鸡胴体内部粪便污染物[J].江西农业大学学报,2011,33(3):573-577.ZHAO Jinhui,TU Dongcheng,OUYANG Jingyi,et al. Detection of internal fecal contaminants of chicken carcasses using hyperspectral imaging technology[J]. Acta Agriculturae Universitatis Jiangxiensis,2011,33(3):573-577.
[75] CHAO K,NOU X,LIU Y,et al. Detection of fecal/ingesta contaminants on poultry processing equipment surfaces by visible and near-infrared reflectance spectroscopy[J]. Applied Engineering in Agriculture,2008,24(1):49-55.
[76] YOON S C,PARK B,LAWRENCE K C,et al. Line-scan hyperspectral imaging system for real-time inspection of poultry carcasses with fecal material and ingesta[J]. Computers&Electronics in Agriculture,2011,79(2):159-168.
[77]赵进辉,吁芳,吴瑞梅,等.基于分段主成分分析与波段比的鸡胴体表面粪便污染物检测[J].激光与光电子学进展,2011,48(7):163-167.
[78] CHAO K. Control interface and tracking control system for automated poultry inspection[J]. Computer Standards&Interfaces,2014,36(2):271-277.
[79]陈坤杰,杨凯,康睿,等.基于机器视觉的鸡胴体表面污染物在线检测技术[J].农业机械学报,2015,46(9):228-232.CHEN Kunjie,YANG Kai,KANG Rui, et al. Online detection technology for contaminants on chicken carcass surface based on machine vision[J]. Transactions of the Chinese Society for Agricultural Machinery,2015,46(9):228-232.
[80]孙宏伟,彭彦昆,林琬.便携式生鲜猪肉多品质参数同时检测装置研发[J].农业工程学报,2015,31(20):268-27.SUN Hongwei,PENG Yankun,LIN Wan. Development of a portable device for simultaneous detection on multi-quality attributes of fresh pork[J]. Transactions of the Chinese Society of Agricultural Engineering,2015,31(20):268-27.
[81]彭彦昆,杨清华,王文秀.基于近红外光谱的猪肉水分在线检测与分级[J].农业机械学报,2018,49(3):347-353.PENG Yankun,YANG Qinghua,WANG Wenxiu. On-line detection and classification of pork moisture based on near-infrared spectra[J]. Transactions of the Chinese Society for Agricultural Machinery,2018,49(3):347-353.
[82]陶琳丽,杨秀娟,邓君明,等.畜禽肉化学成分近红外光谱检测技术研究进展[J].光谱学与光谱分析,2013,33(11):3 002-3 009.TAO Linli,YANG Xiujuan, DENG Junming, et al. Application of near infrared reflectance spectroscopy to predict meat chemical compositions:a review[J]. Spectroscopy and Spectral Analysis,2013,33(11):3 002.-3 009.
[83] GJERLAUG-ENGER E,KONGSRO J,degrd J,et al. Genetic parameters between slaughter pig efficiency and growth rate of different body tissues estimated by computed tomography in live boars of Landrace and Duroc[J]. Animal,2012,6(1):9-18.
[84]罗香,刘波平,张小林,等.偏最小二乘近红外光谱法测定瘦肉脂肪酸组成的研究[J].分析试验室,2007,26(10):25-29.LUO Xiang,LIU Boping,ZHANG Xiaolin,et al. Study on the determination of fatty acids in pork by near infrared spectroscopy based on partial least squares model[J]. Chinese Journal of Analysis Laboratory,2007,26(10):25-29.
[85] González-MartíN I,González-Pérez C,Alvarez-GarcíA N,et al.On-line determination of fatty acid composition in intramuscular fat of Iberian pork loin by NIRs with a remote reflectance fibre optic probe[J]. Meat Science,2005,69(2):243-248.
[86] RIPOCHE A,GUILLARH A S. Determination of fatty acid composition of pork fat by Fourier transform infrared spectroscopy[J].Meat Science,2001,58(3):299-304.
[87] PRIETO N,DUGAN M E R,López-Campos O,et al. Near infrared reflectance spectroscopy predicts the content of polyunsaturated fatty acids and biohydrogenation products in the subcutaneous fat of beef cows fed flaxseed[J]. Meat Science,2012,90(1):43-51.
[88] SIERRA V,ALDAI N,CASTRO P,et al. Prediction of the fatty acid composition of beef by near infrared transmittance spectroscopy[J]. Meat Science,2008,78(3):248-255.
[89] REALINI C E,DUCKETT S K,WINDHA W R. Effect of vitamin C addition to ground beef from grass-fed or grain-fed sources on color and lipid stability, and prediction of fatty acid composition by near-infrared reflectance analysis[J]. Meat Science,2004,68(1):35-43.
[90] CECCHINATO A,DE M M,PENASA M,et al. Genetic analysis of beef fatty acid composition predicted by near-infrared spectroscopy[J]. Journal of Animal Science,2012,90(2):429-38.
[91] MARCHI M D,RIOVANTO R,PENASA M,et al. At-line prediction of fatty acid profile in chicken breast using near infrared reflectance spectroscopy[J]. Meat Science,2012,90(3):653-657.
[92] ZHOU L J,WU H,LI J T,et al. Determination of fatty acids in broiler breast meat by near-infrared reflectance spectroscopy[J].Meat Science,2012,90(3):658-664.
[93] RIOVANTO R,DE M M,CASSANDRO M,et al. Use of near infrared transmittance spectroscopy to predict fatty acid composition of chicken meat[J]. Food Chemistry, 2012, 134(4):2 459-2 464.
[94] BERZAGHI P,DALLE Z A,JANSSON L M,et al. Near-infrared reflectance spectroscopy as a method to predict chemical composition of breast meat and discriminate between different n-3 feeding sources[J]. Poultry Science,2005,84(1):128.
[95] GUY F,PRACHE S,THOMAS A,et al. Prediction of lamb meat fatty acid composition using near-infrared reflectance spectroscopy(NIRS)[J]. Food Chemistry,2011,127(3):1 280-1 286.
[96] HUANG L,ZHAO J,CHEN Q,et al. Nondestructive measurement of total volatile basic nitrogen(TVB-N)in pork meat by integrating near infrared spectroscopy,computer vision and electronic nose techniques[J]. Food Chemistry,2014,145:228-236.
[97]姜沛宏,张玉华,陈东杰,等.基于多源感知信息融合的牛肉新鲜度分级检测[J].食品科学,2016,37(6):161-165.JIANG Peihong, ZHANG Yuhua, CHEN Dongjie, et al. Measurement of beef freshness grading based on multi-sensor information fusion technology[J]. Food Science,2016,37(6):161-165.
[98] NASSY G,黄亚宇,孟庆翔.控制猪肉品质的新型感应器:用于屠宰和加工阶段测定猪胴体组分和评估猪肉品质[J].肉类研究,2015(2):21-24.NASSY G,HUANG Yayu,MENG Qingxiang. New captors for use in controlling pork quality:evaluation of porcine carcass composition and quality traits during slaughter and processing[J]. Meat Research,2015(2):21-24.
[99]陈坤杰,刘浩鲁,於海明,等.基于CT图像技术的三黄鸡胴体物理特征分析[J].农业机械学报,2017,48(7):299-305.CHEN Kunjie,LIU Haolu,YU Haiming,et al. Analysis on physical characteristics of Sanhuang chicken carcasses based on CT image technology[J]. Transactions of the Chinese Society for Agricultural Machinery,2017,48(7):294-300.
[100]沈杰.基于X射线及近红外光谱技术的禽肉品质检测[D].南昌:江西农业大学,2011.SHEN Jie,Inspection of poultry's quality based on X-ray and near infrared spectroscopy technology[D]. Nanchang:Jiangxi Agricultural University,2011.
[101]刘斌,季小阳,付军科,等.利用双能X射线预测绵羊胴体肌内脂肪含量的研究[J].中国畜牧兽医,2015,42(8):2 144-2 149.LIU Bin,JI Xiaoyang,FU Junke,et al. Study on predicting intramuscular fat content of sheep carcass using dual energy X-ray[J]. China Animal Husbandry&Veterinary Medicine,2015,42(8):2 144-2 149.
[102] BRETHOUR J R. Using serial ultrasound measures to generate models of marbling and backfat thickness changes in feedlot cattle[J].Journal of Animal Science,2000,78(78):2 055-2 061.
[103] FORTIN A,TONG A K W,ROBERTSON W M,et al. A novel approach to grading pork carcasses:computer vision and ultrasound[J]. Meat Science,2003,63(4):451-462.
[104] FORTIN A,TONG A K W,ROBERTSON W M. Evaluation of three ultrasound instruments,CVT-2,Ultra Fom 300 and AutoFom for predicting salable meat yield and weight of lean in the primals of pork carcasses[J]. Meat Science,2004,68(4):537-549.
[105]胡晓亮,刘宝林,周国燕.猪肉品质的超声波无损检测实验研究[J].生物医学工程学杂志,2011(4):819-822.HU Xiaoliang,LIU Baolin,ZHOU Guoyan. Experimental study of non-traumatic ultrasonic testing for pork quality[J]. Journal of Biomedical Engineering,2011(4):819-822.
[106]魏彩虹,李宏滨,刘涛,等.应用超声波技术快速预测羊背膘厚和眼肌面积的研究[J].中国畜牧兽医,2011,38(1):236-238.WEI Caihong,LI Hongbin,LIU Tao,et al. Rapid prediction of sheep back fat thickness and eye muscle area in using ultrasonic technology[J]. China Animal Husbandry&Veterinary Medicine,2011,38(1):236-238.
[107]胡宝利.不同年龄秦川牛胴体性状与肉质性状的研究[D].杨陵:西北农林科技大学,2001.HU Baoli. The studies of traits of carcass and meat quality in different age of Qinchuan cattle[D]. Yangling:Northwest A&F University,2011.
[108] BOCHNO R,RYMKIEWICZ J,SZEREMETA J. Regression equations for in vivo estimation of the meat content of Pekin duck carcases[J]. British Poultry Science,2000,41(3):313-317.
[109] KLECZEK K, WAWRO K, WILKIEWICZWAWRO E, et al.Multiple regression equations to estimate the content of breast muscles, meat, and fat in Muscovy ducks[J]. Poultry Science,2006,85(7):1 318-1 326.
[110] CORREIA L R,MITTAL G S,BASIR O A. Ultrasonic detection of bone fragment in mechanically deboned chicken breasts[J]. Innovative Food Science&Emerging Technologies,2008,9(1):109-115.
[2]马丁·冯·文斯兰沃维斯,阿奇姆·许,凯琳·冯·霍莱本,等. Inarco系统米达斯生猪电击晕设备福利性及肉质现场研究第一部分:电流特点和击晕有效性[J].肉类工业,2009(6):2-6.
[3] BANSS. BANSS BRT High frequency constant current stunning technology[EB/OL].[2019-02-09]. https://banss. de/en/#slaughtering-technology-pig-stunning/.
[4]丛明,王冠雄,Peter Xu.屠宰机器人的研究现状与发展[J].机器人技术与应用,2013(1):18-23.CONG Ming,WANG Guanxiong,Peter Xu. Research and development of the slaughter robot[J]. Robot Technique and Application,2013(1):18-23.
[5] BANSS. BANSS DDM[EB/OL].[2019-02-09]. https://banss.de/en/#slaughtering-technology-pig-dehairing/.
[6] BANSS. BANSS BE[EB/OL].[2019-02-09]. https://banss. de/en/#slaughtering-technology-cattle-dehiding/.
[7] TREVELYAN J P. Robots for shearing sheep. Shear magic.[J].Oxford University Press,1992.
[8] PURNELL G. Robotic equipment in the meat industry[J]. Meat Science,1998,49(S1):S297.
[9] FRONTMATEC. Automatic Primal Cutting[EB/OL].[2019-02-09].https://www. frontmatec. com/en/pork-solutions/primal-cutting.
[10]任涛,李伟,徐开春,等.猪体自动劈半机的研发[J].肉类工业,2017(9):49-56.REN Tao, LI Wei, XU Kaichun, et al. Research and development of pig automatic half splitting machine[J]. Meat Industry,2017(9):49-56.
[11] MEYN. Vent cutter M3. 0[EB/OL].[2019-02-09]. https://www. meyn. com/products/evisceration/vent-cutter-m3-0.
[12]王丽红.基于数字化设计的家禽取内脏机关键技术研究[D].北京:中国农业机械化科学研究院,2011.WANG Lihong. Study on key technology of poultry eviscerator based on digital design[D]. Beijing:Chinese Academy of Agricultural Mechanization Science,2011.
[13] MACHINEFABRIEK MEYN B V. Method and apparatus for eviscerating poultry:EP0574617A1[P]. 1992-06-17.
[14] STORK PMT B V. Method and devide for processing a cluster of organs from a slaughtered animal:EP0587253B1[P]. 1997-04-16.
[15] MEYN. Flex Plus cut up line M3. 0[EB/OL].[2019-02-09].https://www. meyn. com/products/cut-up/flex-plus-cut-up-linem3-0.
[16]王猛,李阳阳,叶金鹏.家禽自动掏膛机械手的发展和应用现状[J].农产品加工(学刊),2014(5):62-64.WANG Meng,LI Yangyang,YE Jinpeng. Development and application of the poultry eviscerating manipulator[J]. Academic Periodical of Farm Products Processing,2014(5):62-64.
[17]邢东杰,张奎彪,张文辉.一种家禽自动掏膛机:CN202999192U[P]. 2013-06-19.
[18]王斌. L-10000型家禽自动掏膛生产线被评为全国重大装备首台套示范项目[N].中国工业报,2015-07-23.
[19]王丽红,阎楚良,叶金鹏,等. QNZ15型家禽自动取内脏机设计与试验[J].农业机械学报,2010,41(S1):220-224.WANG Lihong,YAN Chuliang,YE Jinpeng,et al. Design and experiment of QNZ15 automatic poultry eviserator[J]. Transactions of the Chinese Society for Agricultural Machinery,2010,41(S1):220-224.
[20]鲍秀兰,张磊,王树才.家禽净膛机械手末端执行器的设计及运动学分析[J].华中农业大学学报,2017,36(4):117-124.BAO Xiulan,ZHANG Lei,WANG Shucai. Design and kinematics analyses of manipulator end effector for eviscerated poultry[J].Journal of Huazhong Agricultural University,2017,36(4):117-124.
[21]熊利荣,于阳,王树才.带有触觉系统的家禽屠宰净膛机械手的设计[J].华中农业大学学报,2016,35(6):142-146.XIONG Lirong, YU Yang, WANG Shucai. Designing intelligent manipulator with haptic system for poultry slaughtering and evisceration[J]. Journal of Huazhong Agricultural University,2016,35(6):142-146.
[22] FOSS. FoodScan[EB/OL].[2019-02-09]https://www. fossanalytics. com/zh-cn/products/foodscan-meat-analyser.
[23] FRONTMATEC. Carcass Grading[EB/OL].[2019-02-09]. https://www. frontmatec. com/en/pork-solutions/clean-line-chillroom/carcass-grading.
[24] Uni Green Scheme. Destron PG-100 Electronic Grading Probe Kit For Pork Meat Testing Lab Equipment[EB/OL].[2019-02-09].https://shop. unigreenscheme. co. uk/other-lab-equipment/destronpg-100-electronic-grading-probe-kit-for-pork-meat-testing-lab-equipment-fyyn8.
[25] SATHER-AP, JONES-SDM, ROBERTSON-WM. The effect of genotype on pre-dicted lean yield in heavy pig carcasses using the Hennessy grading probe,the DESTRON PG-100 and Fat-O-Meat'er electronic grading probes[J]. Canadian Journal of Animal Science,1995,69:93-101.
[26] HENNESSY. HGS world's leading meat grading system[EB/OL].[2019-02-09]. http://www. hennessy-technology. com/.
[27] KEMPSTER A J. An evaluation of the hennessy grading probe and the SFK Fat-O-Meater for use in pig carcass classification and grading[J]. Animal Science,1985,40(2):323-329.
[28] GISPERT M,GOU P,DIESTER A,et al. Bias and future trends of pig carcass classification methods[J]. Food Chemistry,2000,69(4):457-460.
[29]丁有河,王华,王道路,等.畜胴体自动分级系统开发[J].肉类工业,2017(6):46-48.DING Youhe,WANG Hua,WANG Daolu,et al. Development of automatic grading system of livestock carcass[J]. Meat Industry,2017(6):46-48.
[30] SATHER A P,DRC B,SDM J. Real-time ultrasound image analysis for the estimation of carcass yield and pork quality[J]. Canadian Veterinary Journal La Revue Veterinaire Canadienne,1996,76(1):55-62.
[31] FRONTMATEC. AutoFom[EB/OL].[2019-02-09]. https://www. frontmatec. com/en/other/instruments/carcass-grading-traceability/autofom-iii.
[32] GOLDENBERG A A,ANANTHANARAYANAN S P. An approach to automation of pork grading[J]. Food Research International,1994,27(2):191-193.
[33] UNIPACK.Техническийвзгляднапродукты[EB/OL].(2013-10-11)[2019-02-09]. https://article. unipack. ru/46991/.
[34] TAKATANI S,GRAHAM M D. Theoretical analysis of diffuse reflectance from a two-layer tissue model[J]. IEEE Transactions on Biomedical Engineering,1979,BME-26(12):656-664.
[35] KAY S,HEINAR S,HEINZ D,et al. A portable 671 nm Raman sensor system for rapid meat spoilage identification[J]. Vibrational Spectroscopy,2012,62:70-76.
[36]李翠玲,彭彦昆,汤修映.基于多光谱成像技术的猪肉新鲜度无损快速检测装置[J].农业机械学报,2012,43(S1):202-206.LI Cuiling,PENG Yankun,TANG Xiuying. Device for rapid nondestructive detection of pork freshness based on multispectral imaging technology[J]. Transactions of the Chinese Society for Agricultural Machinery,2012,43(S1):202-206.
[37]海铮,基于电子鼻的牛肉新鲜度检测[D].杭州:浙江大学,2006.HAI Zheng. Detection of beef freshness by an electronic nose[D].Hangzhou:Zhejiang University,2006.
[38]林琬,彭彦昆,王彩萍.便携式生鲜肉品质无损快速检测装置的设计[J].农业工程学报,2014,30(7):243-249.LIN Wan,PENG Yankun,WANG Caiping. Design of portable device for rapid nondestructive detection of fresh meat quality[J].Transactions of the Chinese Society of Agricultural Engineering,2014,30(7):243-249.
[39]林琬.轻简便携式生鲜肉品质无损检测装置的研发[D].北京:中国农业大学,2014.LIN Wan. Development of lightweight portable device for rapid nondestructive detection of fresh meat quality[D]. Beijing:China Agricultural University,2014.
[40]魏文松,彭彦昆.手持式生鲜肉品质参数无损检测装置研究[J].农业机械学报,2016,47(S1):324-331.WEI Wensong,PENG Yankun. Research on hand-held device for nondestructive detection of meat quality parameters[J]. Transactions of the Chinese Society for Agricultural Machinery,2016,47(S1):324-331.
[41] GERRARD D E,GAO X,TAN J. Beef marbling and color score determination by image processing[J]. Journal of Food Science,1996,61(1):145-148.
[42] CANNELL R C,TATUM J D,BELK K E,et al. Dual-component video image analysis system(VIASCAN)as a predictor of beef carcass red meat yield percentage and for augmenting application of USDA yield grades[J]. Journal of Animal Science,1999,77(11):2 942-2 950.
[43] SHIRANITA K,HAYASHI K,OTSUBO A,et al. Grading meat quality by image processing[J]. Pattern Recognition,2000,33(1):97-104.
[44] FRONTMATEC. Carcass Grading[EB/OL].[2019-02-09]. https://www. frontmatec. com/en/beef-solutions/clean-line-chill-room/carcass-grading.
[45]陈丽,张德权.羊胴体分级技术研究现状及趋势[J].食品科技,2010(9):146-150.CHEN Li,ZHANG Dequan. The present situation and trends of the mutton classification technology[J]. Food Science and Technology,2010(9):146-150.
[46]郭楠,王丽红,丁有河,等.气动式羊胴体自动分级系统开发[J].肉类工业,2017(11):49-51.GUO Nan,WANG Lihong,DING Youhe,et al. Development of pneumatic type automatic classification system of sheep carcass[J].Meat Industry,2017(11):49-51.
[47] MEYN. Bird counter vision M1. 0[EB/OL].[2019-02-09]. https://www. meyn. com/products/slaughtering/bird-counter-visionm1-0.
[48] LINCO. Classif EYE[EB/OL].[2019-02-09]. https://www. baader.com/en/products/poultry_processing/distribution/classifeye/index. html.
[49]王丽红,叶金鹏,王子戡,等.畜禽胴体分级技术[J].肉类工业,2014(10):37-41.WANG Lihong,YE Jinpeng,WANG Zikan,et al. Grading technology of livestock and poultry carcass[J] Meat Industry,2014(10):37-41.
[50]张奎彪.中国家禽加工设备“十三五”期间发展趋势[J].肉类工业,2016(11):35-41.ZHANG Kuibiao. The development trend of Chinese poultry processing equipment during the 13th Five-Year Plan period[J]. Meat Industry,2016(11):35-41.
[51]毕然,王家敏,张建喜,等.生猪屠宰监管技术系统设计与实现研究[J].中国农学通报,2012,28(17):57-62.BI Ran,WANG Jiamin,ZHANG Jianxi,et al. Design and application of safe quality supervision system for live-pig slaughtering[J]. Chinese Agricultural Science Bulletin,2012,28(17):57-62.
[52]姬五胜,郭宏,张丰臣.基于Zig Bee和RFID技术的生猪屠宰可溯源系统的设计与实现[J].天津职业技术师范大学学报,2014,24(2):1-6.JI Wusheng,GUO Hong, ZHANG Fengchen. Design and implement of pig slaughtering traceability system based on RFID and ZigBee technology[J]. Journal of Tianjin University of Technology and Education,2014,24(2):1-6.
[53]陈长喜,张宏福,飞颉经纬.肉鸡产业技术体系生产监测与产品质量可追溯平台设计[J].农业机械学报,2010,41(8):100-106.CHEN Changxi, ZHANG Hongfu, FEI Xiejingwei. Traceability platform design of production monitoring and products quality for broilers industry technology system[J]. Transactions of the Chinese Society for Agricultural Machinery,2010,41(8):100-106.
[54]王树才,陶凯,李航.基于机器视觉定位的家禽屠宰净膛系统设计与试验[J].农业机械学报,2018,49(1):335-343.WANG Shucai, TAO Kai, LI Hang. Design and experiment of poultry eviscerator system based on machine vision positioning[J].Transactions of the Chinese Society for Agricultural Machinery,2018,49(1):335-343.
[55] MCDONALD T P,CHEN Y R. Visual characterization of marbling in beef ribeyes and its relationship to taste parameters[J]. Transactions of the Asae,1990,34(6):2 499-2 504.
[56] LI J,TAN J,MARTZ F A,et al. Image texture features as indicators of beef tenderness[J]. Meat Science,1999,53(1):17.
[57] LI J,TAN J,SHATADAL P. Classification of tough and tender beef by image texture analysis[J]. Meat Science,2001,57(4):341-346.
[58] GERRARD D E,GAO X,TAN J. Beef marbling and color score determination by image processing[J]. Journal of Food Science,1996,61(1):145-148.
[59]罗明,朱砺.屠宰加工线上瘦肉型猪胴体瘦肉率估测方法的研究[J].四川农业大学学报,2002,20(2):153-155.LUO Ming,ZHU Li. Study on estimating lean percent of carcass in slaughterhouse[J]. Journal of Sichuan Agricultural University,2002,20(2):153-155.
[60] AMWYLE,CANNELL R C,BELK K E,et al. An evaluation of the pro-totype portable hunterLab video imaging system as a tool to predict tenderness of beef carcasses using objective measure of lean and fatcolor[R]. BeefProgram Report,1999.
[61]于铂,郑丽敏,任发政,等.利用图像处理技术估算猪肉等级评价指标的应用研究[J].肉类研究,2004(3):41-44.
[62] GWARTNEY B L,GAO X,TAN J,et al. Determining fat content in ground beef via color image processing[J]. Journal of Muscle Foods,1996,7(4):11.
[63]郑丽敏,于铂,唐毅,等.利用图像处理技术自动估算猪胴体参数[J].计算机应用研究,2007,24(1):203-206.ZHENG Limin,YU Bo,TANG Yi,et al. Evaluating parameters of hogs's carcass using image processing technology[J]. Application Research of Computers,2007,24(1):203-206.
[64]唐毅.基于机器视觉技术的猪胴体等级在线评定系统[D].北京:中国农业大学,2006.TANG Yi. Online system evaluating pork carcass grade based machine vision[D]. Beijing:China Agricultural University,2006.
[65]丁筱玲,吴玉红,周田田,等.基于机器视觉技术的鸡翅质量预测[J].江苏农业科学,2017(9):216-220.
[66]陈坤杰,李航,于镇伟,等.基于机器视觉的鸡胴体质量分级方法[J].农业机械学报,2017,48(6):290-295,372.CHEN Kunjie,LI Hang,YU Zhenwei,et al. Grading of chicken carcass weight based on machine vision[J]. Transactions of the Chinese Society for Agricultural Machinery,2017,48(6):290-295,372.
[67] BERRANG M E,Smith D P,Windham W R,et al. Effect of intestinal content contamination on broiler carcass Campylobacter counts[J]. Journal of Food Protection,2004,67(2):235.
[68] WEIL S E,PARK B,Y. R C,et al. Integration of Visible/NIR spectroscopy and multispectral imaging for poultry carcass inspection[C]//Photonics for Industrial Applications. International Society for Optics and Photonics,1995.
[69] PARK B,YOON S C,WINDHAM W R,et al. Line-scan hyperspectral imaging for real-time in-line poultry fecal detection[J].Sensing&Instrumentation for Food Quality&Safety,2011,5(1):25-32.
[70]孙啸,逄滨,刘德营,等.基于高光谱图像光谱信息的牛肉大理石花纹分割[J].农业机械学报,2013,44(S1):177-181.SUN Xiao,PANG Bin,LIU Deying,et al. Beef marbling segmentation based on hyperspectral imaging[J]. Transactions of the Chinese Society for Agricultural Machinery, 2013, 44(S1):177-181.
[71]廖宜涛,樊玉霞,伍学千,等.猪肉肌内脂肪含量的可见/近红外光谱在线检测[J].农业机械学报,2010,41(9):104-107,137.LIAO Yitao,FAN Yuxia,WU Xueqian,et al. On-line prediction of intramuscular fat content in pork muscle with visible/near-infrared spectroscopy[J]. Transactions of the Chinese Society for Agricultural Machinery,2010,41(9):104-107,137.
[72]张嫱,郑明媛,张伟,等.高光谱成像技术在禽类产品品质无损检测中的研究进展[J].食品工业科技,2013,34(14):358-362.ZHANG Qiang,ZHENG Mingmin,ZHANG Wei,et al. Research progress in nondestructive detection of poultry products quality based on hyperspectral imaging[J]. Science and Technology of Food Industry,2013,34(14):358-362.
[73] NAKARIYAKUL S,CASASENT D P. Improved forward floating selection algorithm for chicken contaminant detection in hyperspectral imagery[C]//Algorithms and Technologies for Multispectral,Hyperspectral,and Ultraspectral Imagery XIII. International Society for Optics and Photonics,2007.
[74]赵进辉,涂冬成,欧阳静怡,等.利用高光谱图像技术检测鸡胴体内部粪便污染物[J].江西农业大学学报,2011,33(3):573-577.ZHAO Jinhui,TU Dongcheng,OUYANG Jingyi,et al. Detection of internal fecal contaminants of chicken carcasses using hyperspectral imaging technology[J]. Acta Agriculturae Universitatis Jiangxiensis,2011,33(3):573-577.
[75] CHAO K,NOU X,LIU Y,et al. Detection of fecal/ingesta contaminants on poultry processing equipment surfaces by visible and near-infrared reflectance spectroscopy[J]. Applied Engineering in Agriculture,2008,24(1):49-55.
[76] YOON S C,PARK B,LAWRENCE K C,et al. Line-scan hyperspectral imaging system for real-time inspection of poultry carcasses with fecal material and ingesta[J]. Computers&Electronics in Agriculture,2011,79(2):159-168.
[77]赵进辉,吁芳,吴瑞梅,等.基于分段主成分分析与波段比的鸡胴体表面粪便污染物检测[J].激光与光电子学进展,2011,48(7):163-167.
[78] CHAO K. Control interface and tracking control system for automated poultry inspection[J]. Computer Standards&Interfaces,2014,36(2):271-277.
[79]陈坤杰,杨凯,康睿,等.基于机器视觉的鸡胴体表面污染物在线检测技术[J].农业机械学报,2015,46(9):228-232.CHEN Kunjie,YANG Kai,KANG Rui, et al. Online detection technology for contaminants on chicken carcass surface based on machine vision[J]. Transactions of the Chinese Society for Agricultural Machinery,2015,46(9):228-232.
[80]孙宏伟,彭彦昆,林琬.便携式生鲜猪肉多品质参数同时检测装置研发[J].农业工程学报,2015,31(20):268-27.SUN Hongwei,PENG Yankun,LIN Wan. Development of a portable device for simultaneous detection on multi-quality attributes of fresh pork[J]. Transactions of the Chinese Society of Agricultural Engineering,2015,31(20):268-27.
[81]彭彦昆,杨清华,王文秀.基于近红外光谱的猪肉水分在线检测与分级[J].农业机械学报,2018,49(3):347-353.PENG Yankun,YANG Qinghua,WANG Wenxiu. On-line detection and classification of pork moisture based on near-infrared spectra[J]. Transactions of the Chinese Society for Agricultural Machinery,2018,49(3):347-353.
[82]陶琳丽,杨秀娟,邓君明,等.畜禽肉化学成分近红外光谱检测技术研究进展[J].光谱学与光谱分析,2013,33(11):3 002-3 009.TAO Linli,YANG Xiujuan, DENG Junming, et al. Application of near infrared reflectance spectroscopy to predict meat chemical compositions:a review[J]. Spectroscopy and Spectral Analysis,2013,33(11):3 002.-3 009.
[83] GJERLAUG-ENGER E,KONGSRO J,degrd J,et al. Genetic parameters between slaughter pig efficiency and growth rate of different body tissues estimated by computed tomography in live boars of Landrace and Duroc[J]. Animal,2012,6(1):9-18.
[84]罗香,刘波平,张小林,等.偏最小二乘近红外光谱法测定瘦肉脂肪酸组成的研究[J].分析试验室,2007,26(10):25-29.LUO Xiang,LIU Boping,ZHANG Xiaolin,et al. Study on the determination of fatty acids in pork by near infrared spectroscopy based on partial least squares model[J]. Chinese Journal of Analysis Laboratory,2007,26(10):25-29.
[85] González-MartíN I,González-Pérez C,Alvarez-GarcíA N,et al.On-line determination of fatty acid composition in intramuscular fat of Iberian pork loin by NIRs with a remote reflectance fibre optic probe[J]. Meat Science,2005,69(2):243-248.
[86] RIPOCHE A,GUILLARH A S. Determination of fatty acid composition of pork fat by Fourier transform infrared spectroscopy[J].Meat Science,2001,58(3):299-304.
[87] PRIETO N,DUGAN M E R,López-Campos O,et al. Near infrared reflectance spectroscopy predicts the content of polyunsaturated fatty acids and biohydrogenation products in the subcutaneous fat of beef cows fed flaxseed[J]. Meat Science,2012,90(1):43-51.
[88] SIERRA V,ALDAI N,CASTRO P,et al. Prediction of the fatty acid composition of beef by near infrared transmittance spectroscopy[J]. Meat Science,2008,78(3):248-255.
[89] REALINI C E,DUCKETT S K,WINDHA W R. Effect of vitamin C addition to ground beef from grass-fed or grain-fed sources on color and lipid stability, and prediction of fatty acid composition by near-infrared reflectance analysis[J]. Meat Science,2004,68(1):35-43.
[90] CECCHINATO A,DE M M,PENASA M,et al. Genetic analysis of beef fatty acid composition predicted by near-infrared spectroscopy[J]. Journal of Animal Science,2012,90(2):429-38.
[91] MARCHI M D,RIOVANTO R,PENASA M,et al. At-line prediction of fatty acid profile in chicken breast using near infrared reflectance spectroscopy[J]. Meat Science,2012,90(3):653-657.
[92] ZHOU L J,WU H,LI J T,et al. Determination of fatty acids in broiler breast meat by near-infrared reflectance spectroscopy[J].Meat Science,2012,90(3):658-664.
[93] RIOVANTO R,DE M M,CASSANDRO M,et al. Use of near infrared transmittance spectroscopy to predict fatty acid composition of chicken meat[J]. Food Chemistry, 2012, 134(4):2 459-2 464.
[94] BERZAGHI P,DALLE Z A,JANSSON L M,et al. Near-infrared reflectance spectroscopy as a method to predict chemical composition of breast meat and discriminate between different n-3 feeding sources[J]. Poultry Science,2005,84(1):128.
[95] GUY F,PRACHE S,THOMAS A,et al. Prediction of lamb meat fatty acid composition using near-infrared reflectance spectroscopy(NIRS)[J]. Food Chemistry,2011,127(3):1 280-1 286.
[96] HUANG L,ZHAO J,CHEN Q,et al. Nondestructive measurement of total volatile basic nitrogen(TVB-N)in pork meat by integrating near infrared spectroscopy,computer vision and electronic nose techniques[J]. Food Chemistry,2014,145:228-236.
[97]姜沛宏,张玉华,陈东杰,等.基于多源感知信息融合的牛肉新鲜度分级检测[J].食品科学,2016,37(6):161-165.JIANG Peihong, ZHANG Yuhua, CHEN Dongjie, et al. Measurement of beef freshness grading based on multi-sensor information fusion technology[J]. Food Science,2016,37(6):161-165.
[98] NASSY G,黄亚宇,孟庆翔.控制猪肉品质的新型感应器:用于屠宰和加工阶段测定猪胴体组分和评估猪肉品质[J].肉类研究,2015(2):21-24.NASSY G,HUANG Yayu,MENG Qingxiang. New captors for use in controlling pork quality:evaluation of porcine carcass composition and quality traits during slaughter and processing[J]. Meat Research,2015(2):21-24.
[99]陈坤杰,刘浩鲁,於海明,等.基于CT图像技术的三黄鸡胴体物理特征分析[J].农业机械学报,2017,48(7):299-305.CHEN Kunjie,LIU Haolu,YU Haiming,et al. Analysis on physical characteristics of Sanhuang chicken carcasses based on CT image technology[J]. Transactions of the Chinese Society for Agricultural Machinery,2017,48(7):294-300.
[100]沈杰.基于X射线及近红外光谱技术的禽肉品质检测[D].南昌:江西农业大学,2011.SHEN Jie,Inspection of poultry's quality based on X-ray and near infrared spectroscopy technology[D]. Nanchang:Jiangxi Agricultural University,2011.
[101]刘斌,季小阳,付军科,等.利用双能X射线预测绵羊胴体肌内脂肪含量的研究[J].中国畜牧兽医,2015,42(8):2 144-2 149.LIU Bin,JI Xiaoyang,FU Junke,et al. Study on predicting intramuscular fat content of sheep carcass using dual energy X-ray[J]. China Animal Husbandry&Veterinary Medicine,2015,42(8):2 144-2 149.
[102] BRETHOUR J R. Using serial ultrasound measures to generate models of marbling and backfat thickness changes in feedlot cattle[J].Journal of Animal Science,2000,78(78):2 055-2 061.
[103] FORTIN A,TONG A K W,ROBERTSON W M,et al. A novel approach to grading pork carcasses:computer vision and ultrasound[J]. Meat Science,2003,63(4):451-462.
[104] FORTIN A,TONG A K W,ROBERTSON W M. Evaluation of three ultrasound instruments,CVT-2,Ultra Fom 300 and AutoFom for predicting salable meat yield and weight of lean in the primals of pork carcasses[J]. Meat Science,2004,68(4):537-549.
[105]胡晓亮,刘宝林,周国燕.猪肉品质的超声波无损检测实验研究[J].生物医学工程学杂志,2011(4):819-822.HU Xiaoliang,LIU Baolin,ZHOU Guoyan. Experimental study of non-traumatic ultrasonic testing for pork quality[J]. Journal of Biomedical Engineering,2011(4):819-822.
[106]魏彩虹,李宏滨,刘涛,等.应用超声波技术快速预测羊背膘厚和眼肌面积的研究[J].中国畜牧兽医,2011,38(1):236-238.WEI Caihong,LI Hongbin,LIU Tao,et al. Rapid prediction of sheep back fat thickness and eye muscle area in using ultrasonic technology[J]. China Animal Husbandry&Veterinary Medicine,2011,38(1):236-238.
[107]胡宝利.不同年龄秦川牛胴体性状与肉质性状的研究[D].杨陵:西北农林科技大学,2001.HU Baoli. The studies of traits of carcass and meat quality in different age of Qinchuan cattle[D]. Yangling:Northwest A&F University,2011.
[108] BOCHNO R,RYMKIEWICZ J,SZEREMETA J. Regression equations for in vivo estimation of the meat content of Pekin duck carcases[J]. British Poultry Science,2000,41(3):313-317.
[109] KLECZEK K, WAWRO K, WILKIEWICZWAWRO E, et al.Multiple regression equations to estimate the content of breast muscles, meat, and fat in Muscovy ducks[J]. Poultry Science,2006,85(7):1 318-1 326.
[110] CORREIA L R,MITTAL G S,BASIR O A. Ultrasonic detection of bone fragment in mechanically deboned chicken breasts[J]. Innovative Food Science&Emerging Technologies,2008,9(1):109-115.
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